Over the past couple of months, I’ve been experimenting with the Model Context Protocol (MCP) — building AI agents and tools around it. While the experience has been promising, I’ve noticed a few areas where MCP could be expanded or improved.

These aren’t critical issues, but adding them would make MCP more complete and developer-friendly.

Here’s my current wishlist:

1. A Standard MCP Server Interface
2. Bidirectional Notifications
3. Built-in or Native Transport Layer

Let’s walk through each of these in more detail.

Good news! I've extended my lightweight AI orchestrator, CleverChatty, to support Retrieval-Augmented Generation (RAG) by integrating it using the Model Context Protocol (MCP).

Quick Recap

• RAG (Retrieval-Augmented Generation) is an AI technique that enhances language models by retrieving relevant external documents (e.g., from databases or vector stores) based on a user’s query. These documents are then used as additional context during response generation, enabling more accurate, up-to-date, and grounded outputs.

• MCP (Model Context Protocol) is a standard for how external systems—such as tools, memory, or document retrievers—communicate with language models. It enables structured, portable, and extensible context exchange, making it ideal for building complex AI systems like assistants, copilots, or agents.

• CleverChatty is a simple AI orchestrator that connects LLMs with tools over MCP and supports external memory. My goal is to expand it to work with modern AI infrastructure—RAG, memory, tools, agent-to-agent (A2A) interaction, and beyond. It’s provided as a library, and you can explore it via the CLI interface: CleverChatty CLI.